A Query Language Perspective on Graph Learning
Summary: Frames graph/relational representation learning as a query language mapping structures to vectors, unifying GNNs and relational encoders. Recasts expressive-power (distinguishability, approximation) results via this lens and outlines DB-centric research connecting query theory with graph ML. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Floris Geerts (University of Antwerp)
BibTeX Citation
@inproceedings{geerts_pods23,
address = {New York, NY, USA},
series = {{PODS} '23},
title = {{A Query Language Perspective on Graph Learning}},
url = {https://dl.acm.org/doi/10.1145/3584372.3589936},
doi = {10.1145/3584372.3589936},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Geerts, Floris},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,166 | Recursive Querying of Neural Networks via Weighted Structures | 2026 | PODS | 5.093636e-05 |
| 10,913 | Inference-friendly Graph Compression for Graph Neural Networks | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 358 | FAQ: Questions Asked Frequently | 2016 | PODS | 0.00020243592 |
| 1,411 | word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data | 2020 | PODS | 0.00010852334 |
| 2,266 | On Functional Aggregate Queries with Additive Inequalities | 2019 | PODS | 8.8391372e-05 |
| 6,814 | Expressive Power of Linear Algebra Query Languages | 2021 | PODS | 5.7657129e-05 |
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